The potential impact of the next-generation COVID-19 mRNA-1283 vaccine in Canada
Bibliographic record
Abstract
With continued high disease burden in vulnerable groups and fiscal responsibility shifting to Canada’s jurisdictions, assessing the economic value of COVID-19 vaccines is critical for optimizing COVID-19 prevention. This study estimated the public health impact and economically justifiable price (EJP) of Moderna’s next-generation COVID-19 vaccine (mRNA-1283) versus no vaccination in Canada, and relative to currently authorized vaccines (mRNA-1273; BNT-162b2). The target population included individuals aged ≥65 years and 12–64 years at high-risk of severe COVID-19 outcomes, consistent with 2025/2026 national guidelines. Analyses were conducted using a static decision-analytic model (1-year horizon) from a publicly-funded healthcare payer perspective. Vaccine efficacy against infection and hospitalization for mRNA-1283 versus no 2024/2025 vaccination was based on mRNA-1283’s pivotal trial and mRNA-1273 real-world data. Clinical outcomes included infections, hospitalizations, deaths, and number needed to vaccinate (NNV); economic outcomes included total costs, quality-adjusted life-years (QALY), and EJP at a $50,000/QALY willingness-to-pay threshold. Sensitivity analyses were performed. Compared to no vaccine, annual vaccination with mRNA-1283 prevented 288,912 symptomatic infections (NNV = 15), 11,710 hospitalizations (NNV = 364), and 2,194 deaths (NNV = 1,944). The EJP for mRNA-1283 was $325 ($230–$771 in sensitivity analyses). Semi-annual dosing (≥65 or ≥80 years) averted additional hospitalizations and deaths compared to annual vaccination. mRNA-1283 prevented an additional 2,873–3,689 hospitalizations and 537–690 deaths compared to currently authorized vaccines. EJPs for mRNA-1283 were $78 and $103 when compared to mRNA-1273 and BNT162b2, respectively. This study does not include indirect effects, and mRNA-1283 effectiveness has not yet been validated in real-world studies. VE waning and incidence estimates are highly uncertain. British and American estimates were used as Canadian data proxies. mRNA-1283 could reduce the COVID-19 clinical burden and provide economic value for the NACI-recommended population, exceeding current mRNA vaccines; COVID-19 program planners may consider supporting access to mRNA-1283 to optimize public health impact.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".